arXiv Open Access 2020

Learning Manifolds for Sequential Motion Planning

Isabel M. Rayas Fernández Giovanni Sutanto Peter Englert Ragesh K. Ramachandran Gaurav S. Sukhatme

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Abstrak

Motion planning with constraints is an important part of many real-world robotic systems. In this work, we study manifold learning methods to learn such constraints from data. We explore two methods for learning implicit constraint manifolds from data: Variational Autoencoders (VAE), and a new method, Equality Constraint Manifold Neural Network (ECoMaNN). With the aim of incorporating learned constraints into a sampling-based motion planning framework, we evaluate the approaches on their ability to learn representations of constraints from various datasets and on the quality of paths produced during planning.

Topik & Kata Kunci

cs.RO cs.CG

Penulis (5)

Isabel M. Rayas Fernández

Giovanni Sutanto

Peter Englert

Ragesh K. Ramachandran

Gaurav S. Sukhatme

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APA MLA BibTeX

Fernández, I.M.R., Sutanto, G., Englert, P., Ramachandran, R.K., Sukhatme, G.S. (2020). Learning Manifolds for Sequential Motion Planning. https://arxiv.org/abs/2006.07746

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Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓